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a transmission radius equal to 30 m, which imposes a maximum of 8 neighbors per node. Nodes periodically generate packets at a rate of 0.5 bps (low traf c scenario, typical of sensor networking). Packet size is equal to 512 B. The buffer size of the sensor nodes varies among 10, 50, and 100 packets. Energy consumption follows the TR1000 speci cations [66]. An ideal awake/asleep schedule is assumed in which nodes consume energy only when transmitting or receiving packets. The channel is ideal (no errors occurs when transmitting) and it has a data rate equal to 250 Kbps. In the case of the data MULE architecture, the number of available MULEs is an important parameter: 1, 2, and 4 data MULEs can be a good starting point. In the case of data MULEs the deployment area is divided into 81 cells. The MULEs move from the center of one cell (source) to the center of another one (destination) according to the random waypoint model [27]. The MULE travels at a speed of 1 m/s. While traveling from the source to the destination cell, a MULE stops at intermediate cells for data collection, gathering packets from all (and only) the sensors in a cell. The sojourn time spent in each cell is 1 s. MULE queues are considered unbounded (in fact, they can contain 1000 packets, which ensure no over ow, i.e., data loss). The sink to which the MULEs deliver the collected packets is (statically) placed at the center of the deployment area. Once at the sink, a MULE stays in its proximity for a time necessary to empty its queue. Meaningful parameters for GMRE and the other heuristics are as follows. The sink moves among 8 8 sink sites (grid deployment) at the speed of 1 m/s. A shortest path-like routing is used to deliver data in a multihop fashion from the sensor to the current site of the sink. The parameter tmin is set to 50 K (best-performing value among the many tested). The parameter dmax is equal to 190 m. In all the experiments, every node generates over 3000 packets during the simulation time. Finally, in order to obtain statistically meaningful results, the same experiment (simulation run) should be performed on different networks for an enough number of times. That is why results are obtained by averaging over experimental outcomes from 100 different network topologies. Table 10.1 gives a bird s-eye view of the results of the comparison between GMRE and the data MULE solutions. When the network traf c is low and the network deployment area size is limited (as considered here), we observed that both GMRE and the data MULEs deliver all
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TABLE 10.1. GMRE and Data MULEs, General Comparison GMRE Data latency Energy consumption Packet delivery ratio Computational needs
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Low High
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Data MULEs
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generated packets to the sink. As a rule of thumb, successful packet delivery for low/medium network traf c is more challenging for the MULE solution than for the multihop approach (GMRE). The packet delivery ratio degrades as the deployment area grows in size, since the inter-arrival time of a MULE at a cell grows and over ow of sensor node queues can. On the other hand, GMRE is always able to deliver packets to the sink. The advantage of using solution data MULEs-like is clear when energy consumption and sensor node computational capability are considered. Given the single-hop nature of data exchange between the sensors and the MULEs, nodes are not required to implement a full protocol stack. Basic physical and MAC layer functions are suf cient for all data communications. This implies lower nodal energy consumption and lower node complexity and cost. In the scenario described above, sensor nodes in a data MULE setting consume one order of magnitude less energy than in the multihop scenario. However, the gain in energy conservation is heavily paid in terms of endto-end data latency. The difference in this case is as high as four orders of magnitude! This is due to (a) the extremely long time it takes for a MULE to visit the same cell twice and (b) the time needed to go back to the sink to deliver the packets. These two factors are of course dependent on the number of MULEs, their speed, and the size of the deployment area. We observed that varying MULEs speed from pedestrian (as shown here) to slow vehicular speed would not lead to improvements of more than one order of magnitude. Only introducing a high number of data MULEs would satisfy the latency requirements of many WSN applications. This is sometimes impossible and is certainly costly. 10.6.2 Experiments for Data MULEs The kind of experiments that are meaningful to perform in the data MULE architecture mainly concern the average number of packet discarded, and hence lost, because of buffer over ow and the sensor-to-sink packet data latency. It is interesting to notice that in order to interpret correctly the experimental results on these fundamental metrics, it is sometimes necessary to investigate other metrics and patterns, such as (a) the inter-arrival times of the MULEs at the different cells and (b) the average sojourn time of a MULE in a cell. Here is an example of experimental investigation in the basic scenario depicted above. We observed that the average number of packets discarded by the sensor nodes in the case of one MULE roaming among sensors with buffer size set to 10 (packets) is not high at all, especially for nodes that are toward the center of the deployment area. There are a few nodes, however, that, given the presence of a single MULE and small buffers, have to drop some packets. In this case the problem is the movement of the MULE itself, given that the random waypoint mobility leads the MULE toward the center of the deployment area with high probability. The longer at the center, the longer the MULE inter-arrival times at the cells along the perimeter. This is the reason for the observed 4.5% packet loss at the nodes in the corners. This behavior is con rmed by an investigation of the inter-arrival times of 1, 2, and 4 MULEs at the same cell. In all the considered scenarios the inter-arrival times values
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